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Opiniones y comentarios de aprendices correspondientes a Fitting Statistical Models to Data with Python por parte de Universidad de Míchigan

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In this course, we will expand our exploration of statistical inference techniques by focusing on the science and art of fitting statistical models to data. We will build on the concepts presented in the Statistical Inference course (Course 2) to emphasize the importance of connecting research questions to our data analysis methods. We will also focus on various modeling objectives, including making inference about relationships between variables and generating predictions for future observations. This course will introduce and explore various statistical modeling techniques, including linear regression, logistic regression, generalized linear models, hierarchical and mixed effects (or multilevel) models, and Bayesian inference techniques. All techniques will be illustrated using a variety of real data sets, and the course will emphasize different modeling approaches for different types of data sets, depending on the study design underlying the data (referring back to Course 1, Understanding and Visualizing Data with Python). During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera....

Principales reseñas

BS

17 de ene. de 2020

I am very thankful to you sir.. i have learned so much great things through this course.

this course is very helpful for my career. i would like to learn more courses from you. thank you so much.

VO

17 de sep. de 2019

Good course, but the last of three was the most difficult one. I hope that it were a good introduction to the fascinating world of statistics and data science

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26 - 50 de 115 revisiones para Fitting Statistical Models to Data with Python

por Walt T S L

20 de nov. de 2020

Great statistical lessons, I did not realize there were more regression-type models besides Ordinary Least Squares, which expanded my learning horizon, and of course, applied using Python Jupyter Notebooks. Python Code was comprehensive and enabled easy following. It was immensely helpful as I did not know how to even begin constructing a linear model study, using independent or dependent data.

por ellie c

15 de ago. de 2020

The most difficult course in this specification! The most important takeaway point of this course is to understand why we choose any model to fit our data, and how to interpret the model. Don't jump into complex math calculation, we got python to do that for us! Dr Brady did a very good job conveying those ideas to us.

p.s the forum has great discussion posts, make sure to use the forum.

por William S

5 de oct. de 2021

I have learnt to applying coding in statistical analysis. I really enjoyed the Week 4 Bayesian Statistics because the use of coding has added new favor to this topic. It makes the study a real science but not something set in the stone (textbook).

por ARVIND K S

7 de abr. de 2020

A great course on how to fit models to data. Very rich on theoretical concepts and equally great on the practical aspects of using python to fine-tune your model, viewing the same each time as you modify data. Very fine course indeed

por Bharti S

18 de ene. de 2020

I am very thankful to you sir.. i have learned so much great things through this course.

this course is very helpful for my career. i would like to learn more courses from you. thank you so much.

por Alvaro F

12 de mar. de 2019

The course is actually pretty good, however the mix between basic subjects (like univariate linear regression) and relatively advanced topics (marginal models) may discourage some students.

por Kylie A

12 de jul. de 2021

Just like the other courses in the specialization, very well thought out and planned! Up to date, great professors . . . couldn't ask for more!

por Varga I K

14 de abr. de 2019

Great review of machine learning used in statistics finished up with some overview on bayesian math.

Enjoyed very much and learnt even more.

por Camila B V

28 de jun. de 2021

Excellent, the explanations were perfect and its theorical focus was the thing why I loved this course.

por Kumar R

12 de ene. de 2021

These whole three certifications lays the foundation for learning Machine Learning a more in-depth way.

por Xinyuan G

15 de jun. de 2020

The specialization covers important practical topics. I am glad to have the opportunity to explore it.

por Alexander B

28 de may. de 2020

Overall really great coure that covers a lot of material in a concise way.

por Tarit G

4 de jul. de 2020

Excellent course! Thanks to the instructors and the team made this MOOC.

por RODRIGO E P M

23 de ago. de 2020

An excellent introductory course to the world of statistical modeling.

por Nicky D

22 de ene. de 2020

Excellent course, really enjoyed the section on Bayesian statistics.

por nipunjeet s g

25 de may. de 2019

Very informative and the example

applications are extremely detailed

por Prabakaran C

17 de mar. de 2020

Have given me CLearcut idea about Mixed-effects and Marginal Models

por Erhan K

17 de ene. de 2022

E​specially the part on Bayesian Statistics are very informative.

por Hrishi P

11 de jun. de 2020

Great practical applications of statistics with Python!

por DIBYA P S

21 de jun. de 2020

good conceptual development , helped lot in learning

por Harish S

27 de ene. de 2019

Content of course was good. Some issue with quiz.

por Appi

23 de sep. de 2019

Very good instructors and very good workload!

por Debabrata A K S

19 de feb. de 2020

Very nice course. Well explained kudos.

por Sumit M

30 de mar. de 2020

Very Very Good For learning Statistics

por JamieLiu

8 de sep. de 2021

Great course ,I learned a lot from it